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International Conference on AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems

CPAIOR 2016: Integration of AI and OR Techniques in Constraint Programming pp 98–107Cite as

Weighted Spanning Tree Constraint with Explanations

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9676))

Abstract

Minimum Spanning Trees (MSTs) are ubiquitous in optimization problems in networks. Even though fast algorithms exist to solve the MST problem, real world applications are usually subject to constraints that do not let us apply such methods directly. In these cases we confront a version of the MST called the “Weighted Spanning Tree” (WST) in which we look for a spanning tree in a graph that satisfies other side constraints and is of minimum cost. In this paper we implement this constraint using a lower bound and learning to accelerate the search and thus reduce the solving time. We show that having this propagator is tremendously beneficial for solvers and we show the benefits of learning.

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Acknowledgement

Diego de Uña thanks “la Caixa” Foundation for partially funding his Ph.D. studies at The University of Melbourne.

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Authors and Affiliations

  1. Department of Computing and Information Systems, The University of Melbourne, Melbourne, Australia

    Diego de Uña, Graeme Gange, Peter Schachte & Peter J. Stuckey

  2. Victoria Laboratory, National ICT Australia, Melbourne, Australia

    Peter J. Stuckey

Authors
  1. Diego de Uña
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  2. Graeme Gange
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  3. Peter Schachte
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  4. Peter J. Stuckey
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Corresponding author

Correspondence to Diego de Uña .

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Editors and Affiliations

  1. Université Laval, Quebec, Québec, Canada

    Claude-Guy Quimper

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© 2016 Springer International Publishing Switzerland

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de Uña, D., Gange, G., Schachte, P., Stuckey, P.J. (2016). Weighted Spanning Tree Constraint with Explanations. In: Quimper, CG. (eds) Integration of AI and OR Techniques in Constraint Programming. CPAIOR 2016. Lecture Notes in Computer Science(), vol 9676. Springer, Cham. https://doi.org/10.1007/978-3-319-33954-2_8

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  • DOI: https://doi.org/10.1007/978-3-319-33954-2_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33953-5

  • Online ISBN: 978-3-319-33954-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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